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#include "precomp.hpp"
#include "_modelest.h"
#include <algorithm>
#include <iterator>
#include <limits>

using namespace std;


CvModelEstimator2::CvModelEstimator2(int _modelPoints, CvSize _modelSize, int _maxBasicSolutions) {
    modelPoints = _modelPoints;
    modelSize = _modelSize;
    maxBasicSolutions = _maxBasicSolutions;
    checkPartialSubsets = true;
    rng = cvRNG(-1);
}

CvModelEstimator2::~CvModelEstimator2() {
}

void CvModelEstimator2::setSeed(int64 seed) {
    rng = cvRNG(seed);
}


int CvModelEstimator2::findInliers(const CvMat* m1, const CvMat* m2,
                                   const CvMat* model, CvMat* _err,
                                   CvMat* _mask, double threshold) {
    int i, count = _err->rows * _err->cols, goodCount = 0;
    const float* err = _err->data.fl;
    uchar* mask = _mask->data.ptr;

    computeReprojError(m1, m2, model, _err);
    threshold *= threshold;
    for (i = 0; i < count; i++) {
        goodCount += mask[i] = err[i] <= threshold;
    }
    return goodCount;
}


CV_IMPL int
cvRANSACUpdateNumIters(double p, double ep,
                       int model_points, int max_iters) {
    if (model_points <= 0) {
        CV_Error(CV_StsOutOfRange, "the number of model points should be positive");
    }

    p = MAX(p, 0.);
    p = MIN(p, 1.);
    ep = MAX(ep, 0.);
    ep = MIN(ep, 1.);

    // avoid inf's & nan's
    double num = MAX(1. - p, DBL_MIN);
    double denom = 1. - pow(1. - ep, model_points);
    if (denom < DBL_MIN) {
        return 0;
    }

    num = log(num);
    denom = log(denom);

    return denom >= 0 || -num >= max_iters * (-denom) ?
           max_iters : cvRound(num / denom);
}

bool CvModelEstimator2::runRANSAC(const CvMat* m1, const CvMat* m2, CvMat* model,
                                  CvMat* mask0, double reprojThreshold,
                                  double confidence, int maxIters) {
    bool result = false;
    cv::Ptr<CvMat> mask = cvCloneMat(mask0);
    cv::Ptr<CvMat> models, err, tmask;
    cv::Ptr<CvMat> ms1, ms2;

    int iter, niters = maxIters;
    int count = m1->rows * m1->cols, maxGoodCount = 0;
    CV_Assert(CV_ARE_SIZES_EQ(m1, m2) && CV_ARE_SIZES_EQ(m1, mask));

    if (count < modelPoints) {
        return false;
    }

    models = cvCreateMat(modelSize.height * maxBasicSolutions, modelSize.width, CV_64FC1);
    err = cvCreateMat(1, count, CV_32FC1);
    tmask = cvCreateMat(1, count, CV_8UC1);

    if (count > modelPoints) {
        ms1 = cvCreateMat(1, modelPoints, m1->type);
        ms2 = cvCreateMat(1, modelPoints, m2->type);
    } else {
        niters = 1;
        ms1 = cvCloneMat(m1);
        ms2 = cvCloneMat(m2);
    }

    for (iter = 0; iter < niters; iter++) {
        int i, goodCount, nmodels;
        if (count > modelPoints) {
            bool found = getSubset(m1, m2, ms1, ms2, 300);
            if (!found) {
                if (iter == 0) {
                    return false;
                }
                break;
            }
        }

        nmodels = runKernel(ms1, ms2, models);
        if (nmodels <= 0) {
            continue;
        }
        for (i = 0; i < nmodels; i++) {
            CvMat model_i;
            cvGetRows(models, &model_i, i * modelSize.height, (i + 1)*modelSize.height);
            goodCount = findInliers(m1, m2, &model_i, err, tmask, reprojThreshold);

            if (goodCount > MAX(maxGoodCount, modelPoints - 1)) {
                std::swap(tmask, mask);
                cvCopy(&model_i, model);
                maxGoodCount = goodCount;
                niters = cvRANSACUpdateNumIters(confidence,
                                                (double)(count - goodCount) / count, modelPoints, niters);
            }
        }
    }

    if (maxGoodCount > 0) {
        if (mask != mask0) {
            cvCopy(mask, mask0);
        }
        result = true;
    }

    return result;
}


static CV_IMPLEMENT_QSORT(icvSortDistances, int, CV_LT)

bool CvModelEstimator2::runLMeDS(const CvMat* m1, const CvMat* m2, CvMat* model,
                                 CvMat* mask, double confidence, int maxIters) {
    const double outlierRatio = 0.45;
    bool result = false;
    cv::Ptr<CvMat> models;
    cv::Ptr<CvMat> ms1, ms2;
    cv::Ptr<CvMat> err;

    int iter, niters = maxIters;
    int count = m1->rows * m1->cols;
    double minMedian = DBL_MAX, sigma;

    CV_Assert(CV_ARE_SIZES_EQ(m1, m2) && CV_ARE_SIZES_EQ(m1, mask));

    if (count < modelPoints) {
        return false;
    }

    models = cvCreateMat(modelSize.height * maxBasicSolutions, modelSize.width, CV_64FC1);
    err = cvCreateMat(1, count, CV_32FC1);

    if (count > modelPoints) {
        ms1 = cvCreateMat(1, modelPoints, m1->type);
        ms2 = cvCreateMat(1, modelPoints, m2->type);
    } else {
        niters = 1;
        ms1 = cvCloneMat(m1);
        ms2 = cvCloneMat(m2);
    }

    niters = cvRound(log(1 - confidence) / log(1 - pow(1 - outlierRatio, (double)modelPoints)));
    niters = MIN(MAX(niters, 3), maxIters);

    for (iter = 0; iter < niters; iter++) {
        int i, nmodels;
        if (count > modelPoints) {
            bool found = getSubset(m1, m2, ms1, ms2, 300);
            if (!found) {
                if (iter == 0) {
                    return false;
                }
                break;
            }
        }

        nmodels = runKernel(ms1, ms2, models);
        if (nmodels <= 0) {
            continue;
        }
        for (i = 0; i < nmodels; i++) {
            CvMat model_i;
            cvGetRows(models, &model_i, i * modelSize.height, (i + 1)*modelSize.height);
            computeReprojError(m1, m2, &model_i, err);
            icvSortDistances(err->data.i, count, 0);

            double median = count % 2 != 0 ?
                            err->data.fl[count / 2] : (err->data.fl[count / 2 - 1] + err->data.fl[count / 2]) * 0.5;

            if (median < minMedian) {
                minMedian = median;
                cvCopy(&model_i, model);
            }
        }
    }

    if (minMedian < DBL_MAX) {
        sigma = 2.5 * 1.4826 * (1 + 5. / (count - modelPoints)) * sqrt(minMedian);
        sigma = MAX(sigma, FLT_EPSILON * 100);

        count = findInliers(m1, m2, model, err, mask, sigma);
        result = count >= modelPoints;
    }

    return result;
}


bool CvModelEstimator2::getSubset(const CvMat* m1, const CvMat* m2,
                                  CvMat* ms1, CvMat* ms2, int maxAttempts) {
    int* idx = (int*)cvStackAlloc(modelPoints * sizeof(idx[0]));
    int i = 0, j, k, idx_i, iters = 0;
    int type = CV_MAT_TYPE(m1->type), elemSize = CV_ELEM_SIZE(type);
    const int* m1ptr = m1->data.i, *m2ptr = m2->data.i;
    int* ms1ptr = ms1->data.i, *ms2ptr = ms2->data.i;
    int count = m1->cols * m1->rows;

    assert(CV_IS_MAT_CONT(m1->type & m2->type) && (elemSize % sizeof(int) == 0));
    elemSize /= sizeof(int);

    for (; iters < maxAttempts; iters++) {
        for (i = 0; i < modelPoints && iters < maxAttempts;) {
            idx[i] = idx_i = cvRandInt(&rng) % count;
            for (j = 0; j < i; j++)
                if (idx_i == idx[j]) {
                    break;
                }
            if (j < i) {
                continue;
            }
            for (k = 0; k < elemSize; k++) {
                ms1ptr[i * elemSize + k] = m1ptr[idx_i * elemSize + k];
                ms2ptr[i * elemSize + k] = m2ptr[idx_i * elemSize + k];
            }
            if (checkPartialSubsets && (!checkSubset(ms1, i + 1) || !checkSubset(ms2, i + 1))) {
                iters++;
                continue;
            }
            i++;
        }
        if (!checkPartialSubsets && i == modelPoints &&
                (!checkSubset(ms1, i) || !checkSubset(ms2, i))) {
            continue;
        }
        break;
    }

    return i == modelPoints && iters < maxAttempts;
}


bool CvModelEstimator2::checkSubset(const CvMat* m, int count) {
    int j, k, i, i0, i1;
    CvPoint2D64f* ptr = (CvPoint2D64f*)m->data.ptr;

    assert(CV_MAT_TYPE(m->type) == CV_64FC2);

    if (checkPartialSubsets) {
        i0 = i1 = count - 1;
    } else {
        i0 = 0, i1 = count - 1;
    }

    for (i = i0; i <= i1; i++) {
        // check that the i-th selected point does not belong
        // to a line connecting some previously selected points
        for (j = 0; j < i; j++) {
            double dx1 = ptr[j].x - ptr[i].x;
            double dy1 = ptr[j].y - ptr[i].y;
            for (k = 0; k < j; k++) {
                double dx2 = ptr[k].x - ptr[i].x;
                double dy2 = ptr[k].y - ptr[i].y;
                if (fabs(dx2 * dy1 - dy2 * dx1) <= FLT_EPSILON * (fabs(dx1) + fabs(dy1) + fabs(dx2) + fabs(dy2))) {
                    break;
                }
            }
            if (k < j) {
                break;
            }
        }
        if (j < i) {
            break;
        }
    }

    return i >= i1;
}


namespace cv {

class Affine3DEstimator : public CvModelEstimator2 {
public:
    Affine3DEstimator() : CvModelEstimator2(4, cvSize(4, 3), 1) {}
    virtual int runKernel(const CvMat* m1, const CvMat* m2, CvMat* model);
protected:
    virtual void computeReprojError(const CvMat* m1, const CvMat* m2, const CvMat* model, CvMat* error);
    virtual bool checkSubset(const CvMat* ms1, int count);
};

}

int cv::Affine3DEstimator::runKernel(const CvMat* m1, const CvMat* m2, CvMat* model) {
    const Point3d* from = reinterpret_cast<const Point3d*>(m1->data.ptr);
    const Point3d* to   = reinterpret_cast<const Point3d*>(m2->data.ptr);

    Mat A(12, 12, CV_64F);
    Mat B(12, 1, CV_64F);
    A = Scalar(0.0);

    for (int i = 0; i < modelPoints; ++i) {
        *B.ptr<Point3d>(3 * i) = to[i];

        double* aptr = A.ptr<double>(3 * i);
        for (int k = 0; k < 3; ++k) {
            aptr[3] = 1.0;
            *reinterpret_cast<Point3d*>(aptr) = from[i];
            aptr += 16;
        }
    }

    CvMat cvA = A;
    CvMat cvB = B;
    CvMat cvX;
    cvReshape(model, &cvX, 1, 12);
    cvSolve(&cvA, &cvB, &cvX, CV_SVD);

    return 1;
}

void cv::Affine3DEstimator::computeReprojError(const CvMat* m1, const CvMat* m2, const CvMat* model, CvMat* error) {
    int count = m1->rows * m1->cols;
    const Point3d* from = reinterpret_cast<const Point3d*>(m1->data.ptr);
    const Point3d* to   = reinterpret_cast<const Point3d*>(m2->data.ptr);
    const double* F = model->data.db;
    float* err = error->data.fl;

    for (int i = 0; i < count; i++) {
        const Point3d& f = from[i];
        const Point3d& t = to[i];

        double a = F[0] * f.x + F[1] * f.y + F[ 2] * f.z + F[ 3] - t.x;
        double b = F[4] * f.x + F[5] * f.y + F[ 6] * f.z + F[ 7] - t.y;
        double c = F[8] * f.x + F[9] * f.y + F[10] * f.z + F[11] - t.z;

        err[i] = (float)sqrt(a * a + b * b + c * c);
    }
}

bool cv::Affine3DEstimator::checkSubset(const CvMat* ms1, int count) {
    CV_Assert(CV_MAT_TYPE(ms1->type) == CV_64FC3);

    int j, k, i = count - 1;
    const Point3d* ptr = reinterpret_cast<const Point3d*>(ms1->data.ptr);

    // check that the i-th selected point does not belong
    // to a line connecting some previously selected points

    for (j = 0; j < i; ++j) {
        Point3d d1 = ptr[j] - ptr[i];
        double n1 = norm(d1);

        for (k = 0; k < j; ++k) {
            Point3d d2 = ptr[k] - ptr[i];
            double n = norm(d2) * n1;

            if (fabs(d1.dot(d2) / n) > 0.996) {
                break;
            }
        }
        if (k < j) {
            break;
        }
    }

    return j == i;
}

int cv::estimateAffine3D(const Mat& from, const Mat& to, Mat& out, vector<uchar>& outliers, double param1, double param2) {
    int count = from.cols * from.rows * from.channels() / 3;

    CV_Assert(count >= 4 && from.isContinuous() && to.isContinuous() &&
              from.depth() == CV_32F && to.depth() == CV_32F &&
              ((from.rows == 1 && from.channels() == 3) || from.cols * from.channels() == 3) &&
              ((to.rows == 1 && to.channels() == 3) || to.cols * to.channels() == 3) &&
              count == (size_t)to.cols * to.rows * to.channels() / 3);

    out.create(3, 4, CV_64F);
    outliers.resize(count);
    fill(outliers.begin(), outliers.end(), (uchar)1);

    vector<Point3d> dFrom;
    vector<Point3d> dTo;

    copy(from.ptr<Point3f>(), from.ptr<Point3f>() + count, back_inserter(dFrom));
    copy(to.ptr<Point3f>(), to.ptr<Point3f>() + count, back_inserter(dTo));

    CvMat F3x4 = out;
    CvMat mask  = cvMat(1, count, CV_8U, &outliers[0]);
    CvMat m1 = cvMat(1, count, CV_64FC3, &dFrom[0]);
    CvMat m2 = cvMat(1, count, CV_64FC3, &dTo[0]);

    const double epsilon = numeric_limits<double>::epsilon();
    param1 = param1 <= 0 ? 3 : param1;
    param2 = (param2 < epsilon) ? 0.99 : (param2 > 1 - epsilon) ? 0.99 : param2;

    return Affine3DEstimator().runRANSAC(&m1, & m2, &F3x4, &mask, param1, param2);
}
